# AI Literacy for Infrastructure Workers

Source: https://savrn.com/institute/courses/aio-1901
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Foundations · AIO

Proposed course specification. Delivery, enrollment, and assessment arrangements require a partner program. Credential references describe intended alignment or pathways; external credentials and academic awards are issued only by the responsible organizations.

40Total hours

24Lecture

16Hands-on lab

NonePrerequisite

—Proposed credential / alignment

16 lab hours 24 lecture hours

This course introduces the vocabulary and physical realities of AI-factory operations to workers entering data-center trades from electrical, mechanical, or general facilities backgrounds. Topics include what a GPU cluster is and why it draws orders of magnitude more power and heat per rack than traditional enterprise compute, the basic training-versus-inference distinction, why liquid cooling has become mandatory rather than optional at current rack densities, and where a facilities worker's job intersects with AI infrastructure (power, cooling, cabling, security) without requiring the worker to program or administer the systems. Students complete a guided tour or digital-twin walk-through of an AI-factory data hall and correctly identify major subsystems.

## What you'll be able to do

1. Identify the major physical subsystems of an AI-factory data hall (GPU racks, cooling distribution units, power distribution, network fabric) on a facility walkthrough or digital twin.
2. Explain why current-generation GPU racks require direct liquid cooling rather than traditional air cooling.
3. Distinguish AI model training workloads from inference workloads in terms of duration, resource pattern, and business purpose.
4. Locate where a facilities trade (electrical, mechanical, security, cabling) intersects with AI-factory operations in a given job scenario.
5. Define core AI-factory vocabulary (GPU, cluster, token, inference, training, liquid cooling, PUE) in plain language.

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Applied AI & Compute Operations

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